Multiview discriminative learning for age-invariant face recognition

Diana Sungatullina, Jiwen Lu, Gang Wang, Pierre Moulin

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In this paper, we propose a new multiview discriminative learning (MDL) method for age-invariant face recognition, which is a challenging and important problem in many practical face recognition systems. Motivated by the fact that local appearance features are more robust to age variations, we first extract three different local feature descriptors including scale invariant feature transform (SIFT), local binary patterns (LBP) and gradient orientation pyramid (GOP) for each face image to exploit the discriminative information. Then, we develop a discriminative learning method with multiview feature representations, called MDL, to project different types of local features into a latent discriminative subspace where the intraclass variation of each feature is minimized, the interclass variation of each feature and the correlation of different features of the same person are maximized, simultaneously, such that more discriminative information can be boosted for recognition. Experimental results on the widely used MORPH and FG-NET face aging datasets are presented to show the efficiency of the proposed approach.

Original languageEnglish (US)
Title of host publication2013 10th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition, FG 2013
DOIs
StatePublished - Aug 20 2013
Event2013 10th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition, FG 2013 - Shanghai, China
Duration: Apr 22 2013Apr 26 2013

Publication series

Name2013 10th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition, FG 2013

Other

Other2013 10th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition, FG 2013
Country/TerritoryChina
CityShanghai
Period4/22/134/26/13

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition

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